Method for providing a training load schedule for peak performance positioning

ABSTRACT

A system for providing a training load schedule for peak performance positioning includes an apparatus for providing a training load schedule for peak performance positioning. The apparatus includes an initial load schedule module that provides an initial load schedule. The apparatus also includes a fatigue level module that detects a fatigue level. In addition, the apparatus includes a dynamic load schedule module that creates and updates a dynamic load schedule by modifying the initial load schedule based on the fatigue level.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 14/142,633, filed Dec. 27, 2013, titled “System and Method forProviding a Training Load Schedule for Peak Performance Positioning,”which is a continuation-in-part of U.S. patent application Ser. No.14/140,414, filed Dec. 24, 2013, titled “System and Method for Providingan Intelligent Goal Recommendation for Activity Level,” which is acontinuation-in-part of U.S. patent application Ser. No. 14/137,942,filed Dec. 20, 2013, titled “System and Method for Providing anInterpreted Recovery Score,” which is a continuation-in-part of U.S.patent application Ser. No. 14/137,734, filed Dec. 20, 2013, titled“System and Method for Providing a Smart Activity Score,” which is acontinuation-in-part of U.S. patent application Ser. No. 14/062,815,filed Oct. 24, 2013, titled “Wristband with Removable ActivityMonitoring Device.” The contents of the Ser. No. 14/142,633 application,the Ser. No. 14/140,414 application, the Ser. No. 14/137,942application, the Ser. No. 14/137,734 application, and the Ser. No.14/062,815 application are incorporated herein by reference in theirentireties.

TECHNICAL FIELD

The present disclosure relates generally to fitness monitoring devices,and more particularly to a system and method for providing a trainingload schedule for peak performance positioning.

DESCRIPTION OF THE RELATED ART

Previous generation fitness tracking devices generally enabled only atracking of activity that accounts for total calories burned. Currentlyavailable fitness tracking devices now add functionality that providesuniversal metabolic equivalent tasks in attempt to guide a user'straining schedule for an upcoming event. One issue is that currentlyavailable fitness tracking devices do not account for the performancestate, or recovery state (or fatigue level), of the user in ascientific, user-specific way to provide the user with a training loadschedule that will position the user in an optimal performance, orrecovery zone, on the day of a scheduled, future event. Another issue isthat currently available solutions do not dynamically update thetraining load schedule in response to measuring the user's actualfatigue (or recovery) levels on an ongoing basis.

BRIEF SUMMARY OF THE DISCLOSURE

In view of the above drawbacks, there exists a long-felt need forfitness monitoring devices that detect a fatigue level in a scientificway and provide a user-specific training load schedule that isdynamically updated based on periodic detection of the fatigue level.Further, there is a need for fitness monitoring devices that incorporatethis dynamically updated load schedule to prepare a user for an event totake place on a specified date.

Embodiments of the present disclosure include systems and methods forproviding a training load schedule for peak performance positioning.

One embodiment involves an apparatus for providing a training loadschedule for peak performance positioning. The apparatus includes aninitial load schedule module that provides an initial load schedule. Theapparatus also includes a fatigue level module that detects a fatiguelevel. In addition, the apparatus includes a dynamic load schedulemodule that creates and updates a dynamic load schedule by modifying theinitial load schedule based on the fatigue level. The initial loadschedule and the dynamic load schedule, in one embodiment, include atleast one of a recommended daily activity level and a recommendedfatigue level.

The dynamic load schedule module, in one embodiment, creates and updatesthe dynamic load schedule when the fatigue level module detects thefatigue level. In a further embodiment, the dynamic load schedule moduleupdates the dynamic load schedule at least once per day. In oneinstance, the dynamic load schedule prepares a user for an event to takeplace on a specified date. The dynamic load schedule module, in anotherinstance, positions the user in an optimal performance zone on thespecified date of the event.

The apparatus for providing a training load schedule, in one embodiment,also includes calendar module that maintains the dynamic load scheduleand the initial load schedule. In one embodiment, the calendar moduledisplays at least one of the dynamic load schedule and the initial loadschedule using a calendar and at least one of a color-codingrepresentation and a numerical representation. In various embodiments,at least one of the initial load schedule module, the fatigue levelmodule, and the dynamic load schedule module is embodied in a wearablesensor.

One embodiment involves a method for providing a training load schedulefor peak performance positioning. The method includes providing aninitial load schedule. The method also includes detecting a fatiguelevel. In addition, the method includes creating and updating a dynamicload schedule by modifying the initial load schedule based on thefatigue level. The initial load schedule and the dynamic load schedule,in one embodiment, include at least one of a recommended daily activitylevel and a recommended fatigue level.

Creating and updating the dynamic load schedule, in one embodiment,occurs in response to detecting the fatigue level. In a furtherembodiment, updating the dynamic load schedule occurs at least once perday. In one instance, the dynamic load schedule prepares a user for anevent to take place on a specified date. Creating and updating thedynamic load schedule, in another instance, positions the user in anoptimal performance zone on the specified date of the event.

The method for providing a training load schedule, in one embodiment,also includes maintaining the initial load schedule and the dynamic loadschedule in a calendar. In one illustrative case, the method includesdisplaying the initial load schedule and the dynamic load schedule usingthe calendar and at least one of a color-coding representation and anumerical representation. In a further embodiment, the method includesreceiving an external dynamic load schedule and comparing the dynamicload schedule to the external dynamic load schedule.

In various embodiments, at least one of the operations of providing theinitial load schedule, detecting the fatigue level, and creating andupdating the dynamic load schedule by modifying the initial loadschedule based on the fatigue level includes using a sensor configuredto be attached to the body of a user.

One embodiment of the disclosure includes a system for providing atraining load schedule for peak performance positioning. The systemincludes a processor and at least one computer program residing on theprocessor. The computer program is stored on a non-transitory computerreadable medium having computer executable program code embodiedthereon. The computer executable program code is configured to providean initial load schedule. The computer executable program code is alsoconfigured to detect a fatigue level. In addition, the computerexecutable program code is configured to create and update a dynamicload schedule by modifying the initial load schedule based on thefatigue level.

Other features and aspects of the disclosure will become apparent fromthe following detailed description, taken in conjunction with theaccompanying drawings, which illustrate, by way of example, the featuresin accordance with embodiments of the disclosure. The summary is notintended to limit the scope of the disclosure, which is defined solelyby the claims attached hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure, in accordance with one or more variousembodiments, is described in detail with reference to the followingfigures. The figures are provided for purposes of illustration only andmerely depict typical or example embodiments of the disclosure.

FIG. 1 illustrates a cross-sectional view of a wristband and electronicmodules of an example activity monitoring device.

FIG. 2 illustrates a perspective view of an example activity monitoringdevice.

FIG. 3 illustrates a cross-sectional view of an example assembledactivity monitoring device.

FIG. 4 illustrates a side view of an example electronic capsule.

FIG. 5 illustrates a cross-sectional view of an example electroniccapsule.

FIG. 6 illustrates perspective views of wristbands as used in oneembodiment of the disclosed activity monitoring device.

FIG. 7 illustrates an example system for providing a training loadschedule.

FIG. 8 illustrates an example apparatus for providing a training loadschedule.

FIG. 9 illustrates another example apparatus for providing a trainingload schedule.

FIG. 10A is an operational flow diagram illustrating an example methodfor providing a training load schedule.

FIG. 10B is an example metabolic loading table

FIG. 10C is an example activity intensity library.

FIG. 11 is an operational flow diagram illustrating an example methodfor providing a training load schedule including maintaining loadschedules in a calendar.

FIG. 12 is an operational flow diagram illustrating an example methodfor providing a training load schedule including comparing dynamic loadschedules.

FIG. 13 illustrates an example computing module that may be used toimplement various features of the systems and methods disclosed herein.

The figures are not intended to be exhaustive or to limit the disclosureto the precise form disclosed. It should be understood that thedisclosure can be practiced with modification and alteration, and thatthe disclosure can be limited only by the claims and the equivalentsthereof.

DETAILED DESCRIPTION

The present disclosure is directed toward systems and methods forproviding a training load schedule for peak performance positioning. Thedisclosure is directed toward various embodiments of such systems andmethods. In one such embodiment, the systems and methods are directed toa device that provides a training load schedule for peak performancepositioning. According to some embodiments of the disclosure, the devicemay be an electronic capsule embedded in and removable from anattachable device that may be attached to a user. In one embodiment, theattachable device is a wristband. In another embodiment, the attachabledevice includes an activity monitoring device.

FIG. 1 is a diagram illustrating a cross-sectional view of an exampleembodiment of an activity monitoring device. Referring now to FIG. 1, anactivity monitoring device comprises electronic capsule 200 andwristband 100. Electronic capsule 200 comprises wrist biosensor 210,finger biosensor 220, battery 230, one or more logic circuits 240, andcasing 250.

In some embodiments, one or more logic circuits 240 comprise anaccelerometer, a wireless transmitter, and circuitry. Logic circuits 240may further comprise a gyroscope. Logic circuits 240 may be configuredto process electronic input signals from biosensors 210 and 220 and fromthe accelerometer, store the processed signals as data, and output thedata using the wireless transmitter. The transmitter is configured tocommunicate using available wireless communications standards. Forexample, in some embodiments, the wireless transmitter is a BLUETOOTHtransmitter, a Wi-Fi transmitter, a GPS transmitter, a cellulartransmitter, or a combination thereof. In another embodiment, thewireless transmitter further comprises a wired interface (e.g. USB,fiber optic, HDMI, etc.) for communicating stored data.

Logic circuits 240 are electrically coupled to wrist biosensor 210 andfinger biosensor 220. In addition, logic circuits 240 are configured toreceive and process a plurality of electric signals from each of wristbiosensor 210 and finger biosensor 220. In some embodiments, theplurality of electric signals comprise an activation time signal and arecovery time signal such that logic circuits 240 process the pluralityof signals to calculate an activation recovery interval equal to thedifference between the activation time signal and the recovery timesignal. In some embodiments, the plurality of signals compriseelectro-cardio signals from a heart, and logic circuits 240 process theelectro-cardio signals to calculate and store an RR-interval, and theRR-interval is used to calculate and store a heart rate variability(HRV) value. Here, the RR-interval is equal to the delta in time betweentwo R-waves, where the R-waves are the electro-cardio signals generatedby a ventricle contraction in the heart.

In some embodiments, logic circuits 240 further detect and store metricssuch as the amount of physical activity, sleep, or rest over a recentperiod of time, or the amount of time without physical activity over arecent period of time. Logic circuits 240 may then use the HRV, or theHRV in combination with said metrics, to calculate a fatigue level. Forexample, logic circuits 240 may detect the amount of physical activityand the amount of sleep a user experienced over the last 48 hours,combine those metrics with the user's HRV, and calculate a fatigue levelof between 1 and 10, and the fatigue level may indicate the user'sphysical condition and aptitude for further physical activity that day.The fatigue level may also be calculated on a scale of between 1 and100, or any other scale or range. The fatigue level may also berepresented on a descriptive scale; for example, low, normal, and high.

In some embodiments, finger biosensor 220 and wrist biosensor 210 arereplaced or supplemented by a single biosensor. In one such embodiment,the single biosensor is an optical biosensor such as a pulse oximeterconfigured to detect blood oxygen saturation levels. The pulse oximetermay then output a signal to logic circuits 240 indicating a detectedcardiac cycle phase, and logic circuits 240 may use cardiac cycle phasedata to calculate an HRV value.

Wristband 100 comprises material 110 configured to encircle a humanwrist. In one embodiment, wristband 100 is adjustable. Cavity 120 isnotched on the radially inward facing side of wristband 100 and shapedto substantially the same dimensions as the profile of electroniccapsule 200. In addition, aperture 130 is located in material 110 withincavity 120. Aperture 130 is shaped to substantially the same dimensionsas the profile of finger biosensor 220. The combination of cavity 120and aperture 130 is designed to detachably couple to electric capsule200 such that, when electric capsule 200 is positioned inside cavity120, finger biosensor 220 protrudes through aperture 130. Electroniccapsule 200 may further comprise one or more magnets 260 configured tosecure electronic capsule 200 to cavity 120. Magnets 260 may beconcealed in casing 250. Cavity 120 may be configured to conceal magnets260 when electronic capsule 200 detachably couples to the combination ofcavity 120 and aperture 130.

Wristband 100 may further comprise steel strip 140 concealed in material110 within cavity 120. In this embodiment, when electronic capsule 200is positioned within cavity 120, one or more magnets 260 are attractedto steel strip 140 and pull electronic capsule 200 radially outward withrespect to wristband 100. The force provided by magnets 260 maydetachably secure electronic capsule 200 inside cavity 120. In furtherembodiments, electronic capsule 200 is positioned inside cavity 120 andaffixed using a form-fit, press-fit, snap-fit, friction-fit, VELCRO, orother temporary adhesion or attachment technology.

FIG. 2 illustrates a perspective view of one embodiment of the disclosedactivity monitoring device, in which wristband 100 and electroniccapsule 200 are unassembled. FIG. 3 illustrates a cross-sectional viewof one embodiment of a fully assembled wristband 100 with removableathletic monitoring device. FIG. 4 illustrates a side view of electroniccapsule 200 according to one embodiment of the disclosure. FIG. 5illustrates a cross-sectional view of electronic capsule 200. FIG. 6 isa perspective view of two possible variants of wristband 100 accordingto some embodiments of the disclosure. Wristbands 100 may be constructedwith different dimensions, including different diameters, widths, andthicknesses, in order to accommodate different human wrist sizes anddifferent preferences.

In some embodiments of the disclosure, electronic capsule 200 isdetachably coupled to a cavity on a shoe and/or a sock. In otherembodiments, electronic capsule 200 is detachably coupled to sportsequipment. For example, electronic capsule 200 may be detachably coupledto a skateboard, a bicycle, a helmet, a surfboard, a paddle boat, a bodyboard, a hang glider, or other piece of sports equipment. In theseembodiments, electronic capsule 200 is affixed to the sports equipmentusing magnets. In other embodiments, electronic capsule 200 is affixedusing a form-fit, snap-fit, press-fit, friction-fit suction cup, VELCRO,or other technology that would be apparent to one of ordinary skill inthe art.

In one embodiment of the disclosure, electronic capsule 200 includes anoptical sensor such as a heart rate sensor or oximeter. In thisembodiment, the optical sensor is positioned to face radially inwardtowards a human wrist when wristband 100 is fit on the human wrist. Theoptical sensor, in another example, is separate from electronic capsule200, but still detachably coupled to wristband 100 and electronicallycoupled to the circuit boards enclosed in electronic capsule 200.Wristband 100 and electronic capsule 200 may operate in conjunction witha system for providing a training load schedule for peak performancepositioning.

FIG. 7 is a schematic block diagram illustrating an example system 700for providing a training load schedule for peak performance positioning.System 700 includes apparatus for providing a training load schedule forpeak performance positioning 702, communication medium 704, server 706,and computing device 708.

Communication medium 704 may be implemented in a variety of forms. Forexample, communication medium 704 may be an Internet connection, such asa local area network (“LAN”), a wide area network (“WAN”), a fiber opticnetwork, internet over power lines, a hard-wired connection (e.g., abus), and the like, or any other kind of network connection.Communication medium 704 may be implemented using any combination ofrouters, cables, modems, switches, fiber optics, wires, radio, and thelike. Communication medium 704 may be implemented using various wirelessstandards, such as Bluetooth, Wi-Fi, 4G LTE, etc. One of skill in theart will recognize other ways to implement communication medium 704 forcommunications purposes.

Server 706 directs communications made over communication medium 704.Server 706 may be, for example, an Internet server, a router, a desktopor laptop computer, a smartphone, a tablet, a processor, a module, orthe like. In one embodiment, server 706 directs communications betweencommunication medium 704 and computing device 708. For example, server706 may update information stored on computing device 708, or server 706may send information to computing device 708 in real time.

Computing device 708 may take a variety of forms, such as a desktop orlaptop computer, a smartphone, a tablet, a processor, a module, or thelike. In addition, computing device 708 may be a processor or moduleembedded in a wearable sensor, a bracelets, a smart-watch, a piece ofclothing, an accessory, and so on. For example, computing device 708 maybe substantially similar to devices embedded in electronic capsule 200,which may be embedded in and removable from wristband 100, asillustrated in FIG. 1. Computing device 708 may communicate with otherdevices over communication medium 704 with or without the use of server706. In one embodiment, computing device 708 includes apparatus 702. Invarious embodiments, apparatus 702 may be used to perform variousprocesses described herein.

FIG. 8 is a schematic block diagram illustrating one embodiment of anapparatus for providing a training load schedule for peak performancepositioning 800. Apparatus 800 includes apparatus 702 with initial loadschedule module 802, fatigue level module 804, and dynamic load schedulemodule 806. In addition, a movement monitoring module (not shown) maymonitor a movement to create a metabolic activity score based on themovement and user information. The movement monitoring module will bedescribed below in further detail with regard to various processes.

Initial load schedule module 802 provides an initial load schedule.Initial load schedule module 802 will be described below in furtherdetail with regard to various processes.

Fatigue level module 804 detects a fatigue level. Fatigue level module804 will be described below in further detail with regard to variousprocesses.

Dynamic load schedule module 806 creates and updates a dynamic loadschedule by modifying the initial load schedule based on the fatiguelevel. Dynamic load schedule module 806 will be described below infurther detail with regard to various processes.

FIG. 9 is a schematic block diagram illustrating one embodiment ofapparatus for providing a training load schedule for peak performancepositioning 900. Apparatus 900 includes apparatus 702 with initial loadschedule module 802, fatigue level module 804, and dynamic load schedulemodule 806. Apparatus 900 also includes calendar module 902. In oneembodiment, calendar module 902 maintains the dynamic load schedule andthe initial load schedule. In a further embodiment, calendar module 902displays at least one of the dynamic load schedule and the initial loadschedule using a calendar and at least one of a color-codingrepresentation and a numerical representation. Calendar module 902 willbe described below in further detail with regard to various processes.

In one embodiment, at least one of initial load schedule module 802,fatigue level module 804, dynamic load schedule module 806, and calendarmodule 902 is embodied in a wearable sensor, such as electronic capsule200. In various embodiments, any of the modules described herein areembodied in electronic capsule 200 and connect to other modulesdescribed herein via communication medium 704.

FIG. 10A is an operational flow diagram illustrating example method 1000for providing a training load schedule for peak performance positioningin accordance with an embodiment of the present disclosure. Theoperations of method 1000 create a dynamic load schedule based ondetected fatigue level. This aids in preparing a user for a future eventand in positioning the user in a peak performance zone based on theuser's recovery and fatigue levels. In one embodiment, apparatus 702,wristband 100, and electronic capsule 200 perform various operations ofmethod 1000.

At operation 1002, method 1000 involves providing an initial loadschedule. The initial load schedule may take various forms. For example,the initial load schedule may include a target activity level for a userto achieve for a particular period of time (e.g., day, week, month). Theinitial load schedule may be uniform—i.e., constant over time periods—ormay vary over time periods. In one embodiment, the initial load scheduleincludes a recommended daily activity level as tracked by a metabolicactivity score, described in detail below. For example, the initial loadschedule may include a recommended daily metabolic activity score of2,000 points per day. In another embodiment, the initial load scheduleincludes a recommended fatigue level. The recommended fatigue level maybe a fatigue level that a user attempts to achieve as a result of theuser's activities. For example, the recommended fatigue level may be 60points each day. In both of these embodiments, the initial load scheduleis a metric to which a user may conform or attempt to conform.

In one embodiment, the initial load schedule is provided based onnormative data collected from a group of users. The normative data mayprovide a baseline initial load schedule that is not specific to theuser. By way of example. the normative data may be based on publiclyavailable data, or otherwise aggregated empirical data, related totraining schedules for various evens. One example of such normative datamay include a popular training regimen for a marathon, broken down intotraining regimens, for beginning, average, and expert runners. One ofskill in the art will appreciate the many variations possible withrespect the normative data that may be used to provide the initial loadschedule.

In another embodiment, the initial load schedule is provided afterdetecting the user's fatigue level at least one time and using thatfatigue level, in combination with the normative data, as a baseline forthe initial load schedule. The initial load schedule, in one embodiment,is provided to prepare the user for an event to take place at aspecified date. For example, the initial load schedule, if followed bythe user, may prepare a user to run a marathon that is six months in thefuture, and so on. The initial load schedule module provides the initialload schedule, in one embodiment, by determining the fitness levelrequired for the event, creating a rough estimate of the user's currentfitness level, and determining the amount of time until the event willtake place. Based on the parameters, the initial load schedule candetermine a baseline training schedule for the user.

In one embodiment of method 1000, movement is monitored to create ametabolic activity score based on the movement and user information. Themetabolic activity score, in one embodiment, is created from a set ofmetabolic loadings. The metabolic loadings may be determined byidentifying a user activity type from a set of reference activity typesand by identifying a user activity intensity from a set of referenceactivity intensities. In addition, the metabolic loadings may bedetermined based on information provided by a user (user information).

User information may include, for example, an individual's height,weight, age, gender, and geographic and environmental conditions. Theuser may provide the user information by, for example, a user interfaceof computing device 708, or of electronic capsule 200. User informationmay be determined based on various measurements—for example,measurements of the user's body-fat content or body type. Alternatively,for example, the user information may be determined by an altimeter orGPS, which may be used to determine the user's elevation, weatherconditions in the user's environment, etc. In one embodiment, apparatus702 may obtain user information from the user indirectly. For example,apparatus 702 may collect the user information from a social mediaaccount, from a digital profile, or the like.

The user information, in one embodiment, includes a user lifestyleselected from a set of reference lifestyles. For example, apparatus 702may prompt the user for information about the user's lifestyle (e.g.,via a user interface). Apparatus 702 may prompt the user to determinehow active the user's lifestyle is. Additionally, the user may beprompted to select a user lifestyle from a set of reference lifestyles.The reference lifestyles may include a range of lifestyles, for example,ranging from inactive, on one end, to highly active on the other end. Insuch a case, the reference lifestyles that the user selects from mayinclude sedentary, mildly active, moderately active, and heavily active.

In one instance, the user lifestyle is determined from the user as aninitial matter. For example, upon initiation, apparatus 702 may promptthe user to provide a user lifestyle. In a further embodiment, the useris prompted periodically to select a user lifestyle. In this fashion,the user lifestyle selected may be aligned with the user's actualactivity level as the user's activity level varies over time. In anotherembodiment, the user lifestyle is updated without intervention from theuser.

The metabolic loadings, in one embodiment, are numerical values and mayrepresent a rate of calories burned per unit weight per unit time (e.g.,having units of kcal per kilogram per hour). By way of example, themetabolic loadings may be represented in units of oxygen uptake (e.g.,in milliliters per kilogram per minute). The metabolic loadings may alsorepresent a ratio of the metabolic rate during activity (e.g., themetabolic rate associated with a particular activity type and/or anactivity intensity) to the metabolic rate during rest. The metabolicloadings, may, for example be represented in a metabolic table, such asmetabolic table 1050, illustrated in FIG. 10B. In one embodiment, themetabolic loadings are specific to the user information. For example, ametabolic loading may increase for a heavier user, or for an increasedelevation, but may decrease for a lighter user or for a decreasedelevation.

In one embodiment, the set of metabolic loadings are determined based onthe user lifestyle, in addition to the other user information. Forexample, the metabolic loadings for a user with a heavily activelifestyle may differ from the metabolic loadings for a user with asedentary lifestyle. In this fashion, there may be a greater couplingbetween the metabolic loadings and the user's characteristics.

In various embodiments, a device (e.g., computing device 708) or amodule (e.g., electronic capsule 200 or a module therein) stores orprovides the metabolic loadings. The metabolic loadings may bemaintained or provided by server 706 or over communication medium 704.In one embodiment, a system administrator provides the metabolicloadings based on a survey, publicly available data, scientificallydetermined data, compiled user data, or any other source of data. Insome instances, a movement monitoring module performs theabove-described operations. In various embodiments, the movementmonitoring module includes a metabolic loading module and a metabolictable module that determine the metabolic loading associated with themovement.

In one embodiment, a metabolic table is maintained based on the userinformation. The metabolic loadings in the metabolic table may be basedon the user information. In some cases, the metabolic table ismaintained based on a set of standard user information, in place of orin addition to user information from the user. The standard userinformation may comprise, for example, the average fitnesscharacteristics of all individuals being the same age as the user, thesame height as the user, etc. In another embodiment, instead ofmaintaining the metabolic table based on standard information, if theuser has not provided user information, maintaining the metabolic tableis delayed until the user information is obtained.

As illustrated in FIG. 10B, in one embodiment, the metabolic table ismaintained as metabolic table 1050. Metabolic table 1050 may be storedin computing device 708 or apparatus 702, and may include informationsuch as reference activity types (RATs) 1054, reference activityintensities (RAIs) 1052, and/or metabolic loadings (MLs) 1060. Asillustrated in FIG. 10B, in one embodiment, RATs 1054 are arranged asrows 1058 in metabolic table 1050. Each of a set of rows 1058corresponds to different RATs 1054, and each row 1058 is designated by arow index number. For example, the first RAT row 1058 may be indexed asRAT_0, the second as RAT_1, and so on for as many rows as metabolictable 1050 may include.

The reference activity types may include typical activities, such asrunning, walking, sleeping, swimming, bicycling, skiing, surfing,resting, working, and so on. The reference activity types may alsoinclude a catch-all category, for example, general exercise. Thereference activity types may also include atypical activities, such asskydiving, SCUBA diving, and gymnastics. In one embodiment, a userdefines a user-defined activity by programming computing device 708(e.g., by an interface on electronic capsule 200) with information aboutthe user-defined activity, such as pattern of movement, frequency ofpattern, and intensity of movement. The typical reference activities maybe provided, for example, by metabolic table 1050.

In one embodiment, reference activity intensities 1052 are arranged ascolumns in metabolic table 1050, and metabolic table 1050 includescolumns 1056, each corresponding to different RAIs 1052. Each column1056 is designated by a different column index number. For example, thefirst RAI column 1056 is indexed as RAI_0, the second as RAI_1, and soon for as many columns as metabolic table 1050 may include.

The reference activity intensities include, in one embodiment, a numericscale. For example, the reference activity intensities may includenumbers ranging from one to ten (representing increasing activityintensity). The reference activities may also be represented as a rangeof letters, colors, and the like. The reference activity intensities maybe associated with the vigorousness of an activity. For example, thereference activity intensities may represented by ranges of heart ratesor breathing rates.

In one embodiment, metabolic table 1050 includes metabolic loadings1060. Each metabolic loading 1060 corresponds to a reference activitytype 1058 of the reference activity types 1054 and a reference activityintensity 1056 of the reference activity intensities 1052. Eachmetabolic loading 1060 corresponds to a unique combination of referenceactivity type 1054 and reference activity intensity 1052. For example,in the column and row arrangement discussed above, one of the referenceactivity types 1054 of a series of rows 1058 of reference activitytypes, and one of the reference activity intensities 1052 of a series ofcolumns 1056 of reference activity intensities correspond to aparticular metabolic loading 1060. In such an arrangement, eachmetabolic loading 1060 may be identifiable by only one combination ofreference activity type 1058 and reference activity intensity 1056.

This concept is illustrated in FIG. 10B. As shown, each metabolicloading 1060 is designated using a two-dimensional index, with the firstindex dimension corresponding to the row 1058 number and the secondindex dimension corresponding to the column 1056 number of the metabolicloading 1060. For example, in FIG. 10B, ML_2,3 has a first dimensionindex of 2 and a second dimension index of 3. ML_2,3 corresponds to therow 1058 for RAT_2 and the column 1056 for RAI_3. Any combination ofRAT_M and RAI_N may identify a corresponding ML_M,N in metabolic table1050, where M is any number corresponding to a row 1058 number inmetabolic table 1050 and N is any number corresponding to a column 1056number in metabolic table 1050. By way of example, the referenceactivity type RAT_3 may be “surfing,” and the reference activityintensity RAI_3 may be “4.” This combination in the metabolic table 1050corresponds to metabolic loading 1060 ML_3,3, which may, for example,represent 5.0 kcal/kg/hour (a typical value for surfing). In variousembodiments, some of the above-described operations are performed bymovement monitoring module 802 and some of the operations are performedby a metabolic table module.

Referring again to method 1000, in various embodiments, the movement ismonitored by location tracking (e.g., Global Positioning Satellites(GPS), or a location-tracking device connected to a network viacommunication medium 704). The general location of the user, as well asspecific movements of the user's body, are monitored. For example, themovement of the user's leg in x, y, and z directions may be monitored(e.g., by an accelerometer or gyroscope). In one embodiment, apparatus702 receives an instruction regarding which body part is beingmonitored. For example, apparatus 702 may receive an instruction thatthe movement of a user's wrist, ankle, head, or torso is beingmonitored.

In various embodiments, the movement of the user is monitored and apattern of the movement (pattern) is determined. For example, thepattern may be detected by an accelerometer or gyroscope. The patternmay be a repetition of a motion or a similar motion monitored by themethod 1000; for example, the pattern may be geometric shape (e.g., acircle, line, oval) of repeated movement that is monitored. In somecases, the repetition of a motion in a geometric shape is not repeatedconsistently over time, but is maintained for a substantial proportionof the repetitions of movement. For instance, one occurrence ofelliptical motion in a repetitive occurrence (or pattern) of tencircular motions may be monitored and determined to be a pattern ofcircular motion.

In further embodiments, the geometric shape of the pattern of movementis a three dimensional (3-D) shape. To illustrate, the patternassociated with the wrist of a person swimming the butterfly stroke maybe monitored and analyzed into a geometric shape in three dimensions.The pattern may be complicated, but it may be described in a form can berecognized by method 1000. Such a form may include computer code thatdescribes the spatial relationship of a set of points, along withchanges in acceleration forces that are experienced along those pointsas, for example, a sensor travels throughout the pattern.

In various embodiments, monitoring the pattern includes monitoring thefrequency with which the pattern is repeated (or pattern frequency). Thepattern frequency may be derived from a repetition period of the pattern(or pattern repetition period). The pattern repetition period may be thelength of time elapsing from when a device or sensor passes through acertain point in a pattern and when the device or sensor returns to thatpoint when the pattern is repeated. For example, the sensor may be atpoint x, y, z at time t_0. The device may then move along the trajectoryof the pattern, eventually returning to point x, y, z at time t_1. Thepattern repetition period would be the difference between t_1 and t_0(e.g., measured in seconds). The pattern frequency may be the reciprocalof the pattern repetition period, and may have units of cycles persecond. When the pattern repetition period is, for example, two seconds,the pattern frequency would be 0.5 cycles per second.

In some embodiments, various other inputs may be used to determine theactivity type and activity intensity. For example, monitoring themovement may include monitoring the velocity at which the user is moving(or the user velocity). The user velocity may, for example, have unitsof kilometers per hour. In one embodiment, the user's locationinformation is monitored to determine user velocity. This may be done byGPS, through communication medium 704, and so on. The user velocity maybe distinguished from the speed of the pattern (or pattern speed). Forexample, the user may be running at a user velocity of 10 km/hour, butthe pattern speed of the user's wrist may be 20 km/hour at a given point(e.g., as the wrist moves from behind the user to in front of the user).The pattern speed may be monitored using, for example, an accelerometeror gyroscope.

In one embodiment, the user's altitude is monitored. This may be done,for example, using an altimeter, user location information, informationentered by the user, etc. In another embodiment, the impact the user haswith an object (e.g., the impact of the user's feet with ground) ismonitored. This may be done using an accelerometer or gyroscope. In somecases, the ambient temperature is measured. A group of referenceactivity types may be associated with bands of ambient temperature. Forexample, when the ambient temperature is zero degrees Celsius,activities such as skiing, sledding, and ice climbing are appropriateselections for reference activity types, whereas surfing, swimming, andbeach volleyball may be inappropriate. The ambient humidity may also bemeasured (e.g., by a hygrometer). In some cases, pattern duration (i.e.,the length of time for which particular movement pattern is sustained)is measured.

Monitoring the movement, in one embodiment, is accomplished usingsensors configured to be attached to a user's body. Such sensors mayinclude a gyroscope or accelerometer to detect movement, and aheart-rate sensor, each of which may be embedded in a wristband that auser can wear on the user's wrist or ankle, such as wristband 100.Additionally, various modules and sensors that may be used to performthe above-described operations may be embedded in electronic capsule200. In various embodiments, the above-described operations areperformed by the movement monitoring module.

Method 1000, in one embodiment, involves determining the user activitytype from the set of reference activity types. Once detected, thepattern may be used to determine the user activity type from a set ofreference activity types. Each reference activity type is associatedwith a reference activity type pattern. The user activity type may bedetermined to be the reference activity type that has a referenceactivity type pattern that matches the pattern measured by method 1000.

In some cases, the pattern that matches the reference activity typepattern will not be an exact match, but will be substantially similar.In other cases, the patterns will not even be substantially similar, butit may be determined that the patterns match because they are the mostsimilar of any patterns available. For example, the reference activitytype may be determined such that the difference between the pattern ofmovement corresponding to this reference activity type and the patternof movement is less than a predetermined range or ratio. In oneembodiment, the pattern is looked up (for a match) in a referenceactivity type library. The reference activity type library may beincluded in the metabolic table. For example, the reference type librarymay include rows in a table such as the RAT rows 1058.

In further embodiments, method 1000 involves using the pattern frequencyto determine the user activity type from the set of reference activitytypes. Several reference activity types, however, may be associated withsimilar patterns (e.g., because the wrist moves in a similar patternwhen running versus walking). In such cases, the pattern frequency maybe used to determine the activity type (e.g., because the patternfrequency for running is higher than the pattern frequency for walking).

Method 1000, in some instances, involves using additional information todetermine the activity type of the user. For example, the pattern forwalking may be similar to the pattern for running. The referenceactivity of running may be associated with higher user velocities andthe reference activity of walking with lower user velocities. In thisway, the velocity measured may be used to distinguish two referenceactivity types having similar patterns.

In other embodiments, method 1000 involves monitoring the impact theuser has with the ground and determining that, because the impact islarger, the activity type, for example, is running rather than walking.If there is no impact, the activity type may be determined to be cycling(or other activity where there is no impact). In some cases, thehumidity is measured to determine whether the activity is a water sport(i.e., whether the activity is being performed in the water). Thereference activity types may be narrowed to those that are performed inthe water, from which narrowed set of reference activity types the useractivity type may be determined. In other cases, the temperaturemeasured is used to determine the activity type.

Method 1000 may entail instructing the user to confirm the user activitytype. In one embodiment, a user interface is provided such that the usercan confirm whether a displayed user activity type is correct, or selectthe user activity type from a group of activity types.

In further embodiments, a statistical likelihood for of choices for useractivity type is determined. The possible user activity types are thenprovided to the user in such a sequence that the most likely useractivity type is listed first (and then in descending order oflikelihood). For example, it may be determined that, based on thepattern, the pattern frequency, the temperature, and so on, that thereis an 80% chance the user activity type is running, a 15% chance theuser activity type is walking, and a 5% chance the user activity isdancing. Via a user interface, a list of these possible user activitiesmay be provided such that the user may select the activity type the useris performing. In various embodiments, some of the above-describedoperations are performed by a metabolic loading module.

Method 1000, in some embodiments, also includes determining the useractivity intensity from a set of reference activity intensities. Theuser activity intensity may be determined in a variety of ways. Forexample, the repetition period (or pattern frequency) and user activitytype (UAT) may be associated with a reference activity intensity libraryto determine the user activity intensity that corresponds to a referenceactivity intensity. FIG. 10C illustrates one embodiment whereby thisaspect of method 1000 is accomplished, including reference activityintensity library 1080. Reference activity intensity library 1080 isorganized by rows 1088 of reference activity types 1084 and columns 1086of pattern frequencies 1082. In FIG. 10C, reference activity library1080 is implemented in a table. Reference activity library 1080 may,however, be implemented other ways.

In one embodiment, it is determined that, for user activity type 1084UAT_0 performed at pattern frequency 1082 F_0, the reference activityintensity 1090 is RAI_0,0. For example, UAT 1084 may correspond to thereference activity type for running, a pattern frequency 1082 of 0.5cycles per second for the user activity type may be determined.Reference activity intensity library 1080 may determine, at operation1002, that the UAT 1084 of running at a pattern frequency 1082 of 0.5cycles per second corresponds to an RAI 1090 of five on a scale of ten.In another embodiment, the reference activity intensity 1090 isindependent of the activity type. For example, the repetition period maybe five seconds, and this may correspond to an intensity level of two ona scale of ten.

Reference activity intensity library 1080, in one embodiment, isincluded in metabolic table 1050. In some cases, the measured repetitionperiod (or pattern frequency) does not correspond exactly to arepetition period for a reference activity intensity in metabolic table1050. In such cases, the correspondence may be a best-match fit, or maybe a fit within a tolerance. Such a tolerance may be defined by the useror by a system administrator, for example.

In various embodiments, method 1000 involves supplementing themeasurement of pattern frequency to help determine the user activityintensity from the reference activity intensities. For example, if theuser activity type is skiing, it may be difficult to determine the useractivity intensity because the pattern frequency may be erratic orotherwise immeasurable. In such an example, the user velocity, theuser's heart rate, and other indicators (e.g., breathing rate) may bemonitored to determine how hard the user is working during the activity.For example, higher heart rate may indicate higher user activityintensity. In a further embodiment, the reference activity intensity isassociated with a pattern speed (i.e., the speed or velocity at whichthe sensor is progressing through the pattern). A higher pattern speedmay correspond to a higher user activity intensity.

Method 1000, in one embodiment, determines the user activity type andthe user activity intensity by using sensors configured to be attachedto the user's body. Such sensors may include, for example, a gyroscopeor accelerometer to detect movement, and a heart-rate sensor, each ofwhich may be embedded in a wristband that a user can wear on the user'swrist or ankle, such as wristband 100. Additionally, various sensors andmodules that may be used to preform above-described operations of method1000 may be embedded in electronic capsule 200. In various embodiments,the above-described operations are performed by the movement monitoringmodule.

Method 1000, in one embodiment, includes creating and updating ametabolic activity score based on the movement and the user information.Method 1000 may also include determining a metabolic loading associatedwith the user and the movement. In one embodiment, a duration of theactivity type at a particular activity intensity (e.g., in seconds,minutes, or hours) is determined. The metabolic activity score may becreated and updated by, for example, multiplying the metabolic loadingby the duration of the user activity type at a particular user activityintensity. If the user activity intensity changes, the new metabolicloading (associated with the new user activity intensity) may bemultiplied by the duration of the user activity type at the new useractivity intensity. In one embodiment, the activity score is representedas a numerical value. By way of example, the metabolic activity scoremay be updated by continually supplementing the metabolic activity scoreas new activities are undertaken by the user. In this way, the metabolicactivity score continually increases as the user participates in moreand more activities.

Referring again to FIG. 10A, operation 1004 includes detecting a fatiguelevel. In one embodiment, the fatigue level is the fatigue level of theuser. In one embodiment, the fatigue level is a function of recovery. Invarious embodiments, the fatigue level is described in terms ofrecovery. The fatigue level may be detected in various ways. In oneexample, the fatigue level is detected by measuring a heart ratevariability (HRV) of a user using logic circuits 240 (discussed above inreference in to FIG. 1). Further, representations of fatigue level aredescribed above (e.g., numerical, descriptive, etc.). When the HRV ismore consistent (i.e., steady, consistent amount of time betweenheartbeats), for example, the fatigue level may be higher. In otherwords, the body is less fresh and is less well-rested. When HRV is moresporadic (i.e., amount of time between heartbeats varies largely), thefatigue level may be lower. In various embodiments, the fatigue level isdescribed in terms of an HRV score.

At operation 1004, HRV may be measured in a number of ways (discussedabove in reference in to FIG. 1). Measuring HRV, in one embodiment,involves the combination of wrist biosensor 210 and finger biosensor220. Wrist biosensor 210 may measure the heartbeat in the wrist of onearm while finger sensor 220 measures the heartbeat in a finger of thehand of the other arm. This combination allows the sensors, which in oneembodiment are conductive, to measure an electrical potential throughthe body. Information about the electrical potential provides cardiacinformation (e.g., HRV, fatigue level, heart rate information, and soon), and such information may be processed at operation 1004. In otherembodiments, the HRV is measured using sensors that monitor other partsof the user's body, rather than the finger and wrist. For example, thesensors may monitor the ankle, leg, arm, or torso.

In one embodiment, at operation 1004, the fatigue level is detectedbased solely on the HRV measured. The fatigue level, however, may bebased on other measurements (e.g., measurements monitored by method1000). For example, the fatigue level may be based on the amount ofsleep that is measured for the previous night, the duration and type ofuser activity, and the intensity of the activity determined for aprevious time period (e.g., exercise activity level in the lasttwenty-four hours). By way of example, these factors may includestress-related activities such as work and driving in traffic, which maygenerally cause a user to become fatigued. In some cases, the fatiguelevel is detected by comparing the HRV measured to a reference HRV. Thisreference HRV may be based on information gathered from a large numberof people from the general public. In another embodiment, the referenceHRV is based on past measurements of the user's HRV.

At operation 1004, in one embodiment, the fatigue level is detected onceevery twenty-four hours. This provides information about the user'sfatigue level each day so that the user's activity levels may bedirected according to the fatigue level. In various embodiments, thefatigue level is detected more or less often. Using the fatigue level, auser may determine whether or not an activity is necessary (ordesirable), the appropriate activity intensity, and the appropriateactivity duration. For example, in deciding whether to go on a run, orhow long to run, the user may want to use operation 1004 to assess theuser's current fatigue level. Then, the user may, for example, run for ashorter time if the user is more fatigued, or for a longer time if theuser is less fatigued. In some cases, it may be beneficial to detect thefatigue level in the morning, upon the user's waking up. This mayprovide the user a reference for how the day's activities shouldproceed.

Referring again to FIG. 10A, operation 1006 involves creating andupdating a dynamic load schedule by modifying the initial load schedulebased on the fatigue level. In one embodiment, the initial load scheduleis modified based on the fatigue level to prevent the user from becomingover-fatigued or under-fatigued. If the user becomes over-fatigued, theuser may be too tired and may not be able to achieve peak performance.If the user is under-fatigued, the user may be to recovered and may notbe sharp enough to achieve peak performance. In other words, by avoidingunder- and over-fatigue, the dynamic load schedule positions the user inan optimal performance zone. By creating the dynamic load schedule,method 1000 provides a load schedule that adapts to the user's actualfatigue level. In one embodiment, the dynamic load schedule is, in form,substantially similar to the initial load schedule. For example, thedynamic load schedule may include a recommended daily activitylevel—e.g., in the form of metabolic activity score. In addition, thedynamic load schedule may include a recommended fatigue level.

In one embodiment, by continually updating based on the fatigue level,the dynamic load schedule prepares a user for an event to take place ona specified date. The dynamic load schedule, in one instance, provides arecommendation for activity level to the user, for example, in the formof metabolic activity score. By following the recommendation foractivity level (or recommendation for fatigue level), the user may beable to build up the endurance and strength required for the even takingplace on the specified date.

In addition, being tuned to the user's fatigue level, the dynamic loadschedule, in one embodiment, places the user in peak performance (oroptimal performance zone) and recovery position on the date of thespecified event. In other words, the user may be positioned in arecovery state—or at a fatigue level—in which the user is neitherover-fatigued or under-fatigued. Peak performance (or optimalperformance zone), may correspond to, for example, a fatigue level ofbetween 40 and 60. In such an example, the dynamic load schedule wouldposition the user at a fatigue level of between 40 and 60 on the day ofthe event. For some users, however, the peak performance zone may bedifferent, and method 1000 may determine the user's specific peakperformance and recovery position by tracking the user's performanceover time.

In one embodiment, the initial load schedule is provided by calculatingthe number of days it would take for a typical user to prepare for aspecified event at a future date. In such an embodiment, the user mayhave characteristics different from the assumed user characteristicsused to create the initial load schedule. As a result, the initial loadschedule may not be tailored to the user. The dynamic load schedule,being based on the user's fatigue levels, may be tailored to the user'sactual, physical response from undergoing activity, including restingfrom the activity.

Updating the dynamic load schedule, in one embodiment, occurs inresponse to detecting the fatigue level. This may be done, for example,in real time following the detection of the fatigue level at operation1004. In one instance, the user may not desire for the dynamic loadschedule to be updated in response to detecting the fatigue level. Forexample, if the user suspects that the fatigue level detected isinaccurate—e.g., due to user error—the user may desire to keep thenon-updated dynamic load schedule because the updated version would beinaccurate. In one embodiment, the dynamic load schedule is stored uponcreation (or upon being updated), such that, if the dynamic loadschedule is updated contrary to the user's desire, the dynamic loadschedule may be restored to a past state. The dynamic load schedule, inone embodiment, is updated at least once per day following detection ofthe fatigue level.

FIG. 11 is an operational flow diagram illustrating example method 1100for providing a training load schedule for peak performance positioning.In one embodiment, apparatus 702, wristband 100, and electronic capsule200 perform various operations of method 1100. Method 1100, in variousembodiments, includes the operations of method 1000.

In one embodiment, at operation 1104, method 1100 involves maintainingthe initial load schedule and the dynamic load schedule in a calendar.For example, the initial load schedule and the dynamic load schedule maybe maintained as recommended activity or fatigue levels for each dayrepresented on a week or month calendar. Other variations will beappreciated by one of ordinary skill in the art. The initial loadschedule and the dynamic load schedule, in one embodiment, aremaintained in the calendar with various graphical presentations. Forexample, the graphical presentation may include a line graph spanningmultiple days that shows the recommended load schedule (initial ordynamic). In one instance of the disclosure, the dynamic load scheduleis displayed overlaying the initial load schedule. This example displayprovides a quick comparison between the initial load schedule and thedynamic load schedule.

Referring again to FIG. 11, in one embodiment, method 1100 includesoperation 1106, which involves displaying the initial load schedule andthe dynamic load schedule using the calendar and at least one of acolor-coding representation and a numerical representation. For example,the initial load schedule and the dynamic load schedule may berepresented as a numerical value on the calendar (e.g., dynamic loadschedule for Oct. 12, 2014, may be 2,000).

Moreover, the initial and dynamic load schedules may be, for example,represented using a series of colors to indicate the recommended load.In such an example, red may indicate a high recommended load (i.e., veryactive day), yellow may indicate a moderate load (i.e., normally activeday), and green may indicate a light load (i.e., restful day). Inanother example, the color might indicate whether the user is on pace tobe prepared for the specified event. This provides for at-a-glance,understandable information that the user can rely on to direct theuser's activities. In a further embodiment, the load schedules arepresented using a combination of numerical, color-coded, and graphicalrepresentations.

FIG. 12 is an operational flow diagram illustrating example method 1200for providing a training load schedule for peak performance positioningin accordance with an embodiment of the present disclosure. In oneembodiment, apparatus 702, wristband 100, and electronic capsule 200perform various operations of method 1200. Method 1200, in variousembodiments, includes the operations of method 1100.

In one embodiment, at operation 1204, method 1200 involves receiving anexternal dynamic load schedule. The external dynamic load schedule maybe received in a number of ways (e.g., via communication medium 704).The external dynamic load schedule may be created and updated in amanner similar to the creating and updating of the dynamic load schedule(e.g., at operation 1006). The external dynamic load schedule may befrom a second user, who is any user other than the user. For example,the second user may be a friend or associate of the first user.

In one instance, the external dynamic load schedule is a past dynamicload schedule of the user that is associated with a past event. Forexample, the external dynamic load schedule may be the user's dynamicload schedule for the 2013 Saint George Marathon. In this way, method1200 may provide ghost-training capabilities whereby the user can trainagainst the user's past training regimens. In various embodiments,operation 1204 is performed by dynamic load schedule module 806.

At operation 1206, an embodiment of method 1200 involves comparing thedynamic load schedule to the external dynamic load schedule. Operation1206, in one embodiment, entails displaying a graphical, numerical, orcolor-coded representation of the dynamic load schedule. Method 1200 mayoverlay that representation with a similar representation of theexternal dynamic load schedule. One of ordinary skill in the art willappreciate other ways that method 1200 may compare the load schedules atoperation 1206. In another embodiment, method 1200 compares the dynamicload schedule to multiple external dynamic load schedules associatedwith other users. Operation 1206, in a further embodiment, compares thedynamic load schedule to multiple past dynamic load schedules of theuser that are associated with multiple past events. This provides ametric whereby the user can ghost train against the user's own pastperformance training in a set of past events that may be similar to theupcoming event.

Operation 1206, in other words, allows the user to compare the user'sdynamic load schedule, which is based on the user's fatigue level and aspecified event in the future, to the external dynamic load schedule ofother users, which may be based on the other users' fatigue levels andfuture events specified by the other users. In the case that thespecified event from the user and the specified even from the externalusers are the same event, operation 1206 provides the user with arelative metric for the user's preparation for the specified event. Thismay allow the user to compete against the other users as the user andthe other users train or prepare for an upcoming, scheduled event. Invarious embodiments, operation 1206 is performed dynamic load schedulemodule 806.

In one embodiment, the operations of method 1000, method 1100, andmethod 1200 are performed using sensors configured to be attached to thebody (e.g., the user's body). Such sensors may include a gyroscope oraccelerometer to detect movement, and a heart-rate sensor, each of whichmay be embedded in a wristband that a user can wear on the user's wristor ankle, such as wristband 100, or a device or module such aselectronic capsule 200. Such sensors may be used to perform theoperations of providing the initial load schedule, detecting the fatiguelevel, and creating and updating the dynamic load schedule, and anyother operation disclosed herein.

FIG. 13 illustrates an example computing module that may be used toimplement various features of the systems and methods disclosed herein.In one embodiment, the computing module includes a processor and a setof computer programs residing on the processor. The set of computerprograms is stored on a non-transitory computer readable medium havingcomputer executable program code embodied thereon. The computerexecutable code is configured to provide an initial load schedule. Thecomputer executable code is further configured to detect a fatiguelevel. The computer executable code is also configured to create andupdate a dynamic load schedule by modifying the initial load schedulebased on the fatigue level.

The example computing module may be used to implement these variousfeatures in a variety of ways, as described above with reference to themethods illustrated in FIGS. 10A, 10B, 10C, 11, and 12, and as will beappreciated by one of ordinary skill in the art.

As used herein, the term module might describe a given unit offunctionality that can be performed in accordance with one or moreembodiments of the present application. As used herein, a module mightbe implemented utilizing any form of hardware, software, or acombination thereof. For example, one or more processors, controllers,ASICs, PLAs, PALs, CPLDs, FPGAs, logical components, software routinesor other mechanisms might be implemented to make up a module. Inimplementation, the various modules described herein might beimplemented as discrete modules or the functions and features describedcan be shared in part or in total among one or more modules. In otherwords, as would be apparent to one of ordinary skill in the art afterreading this description, the various features and functionalitydescribed herein may be implemented in any given application and can beimplemented in one or more separate or shared modules in variouscombinations and permutations. Even though various features or elementsof functionality may be individually described or claimed as separatemodules, one of ordinary skill in the art will understand that thesefeatures and functionality can be shared among one or more commonsoftware and hardware elements, and such description shall not requireor imply that separate hardware or software components are used toimplement such features or functionality.

Where components or modules of the application are implemented in wholeor in part using software, in one embodiment, these software elementscan be implemented to operate with a computing or processing modulecapable of carrying out the functionality described with respectthereto. One such example computing module is shown in FIG. 13. Variousembodiments are described in terms of this example-computing module1300. After reading this description, it will become apparent to aperson skilled in the relevant art how to implement the applicationusing other computing modules or architectures.

Referring now to FIG. 13, computing module 1300 may represent, forexample, computing or processing capabilities found within desktop,laptop, notebook, and tablet computers; hand-held computing devices(tablets, PDA's, smart phones, cell phones, palmtops, smart-watches,smart-glasses etc.); mainframes, supercomputers, workstations orservers; or any other type of special-purpose or general-purposecomputing devices as may be desirable or appropriate for a givenapplication or environment. Computing module 1300 might also representcomputing capabilities embedded within or otherwise available to a givendevice. For example, a computing module might be found in otherelectronic devices such as, for example, digital cameras, navigationsystems, cellular telephones, portable computing devices, modems,routers, WAPs, terminals and other electronic devices that might includesome form of processing capability.

Computing module 1300 might include, for example, one or moreprocessors, controllers, control modules, or other processing devices,such as a processor 1304. Processor 1304 might be implemented using ageneral-purpose or special-purpose processing engine such as, forexample, a microprocessor, controller, or other control logic. In theillustrated example, processor 1304 is connected to a bus 1302, althoughany communication medium can be used to facilitate interaction withother components of computing module 1300 or to communicate externally.

Computing module 1300 might also include one or more memory modules,simply referred to herein as main memory 1308. For example, preferablyrandom access memory (RAM) or other dynamic memory, might be used forstoring information and instructions to be executed by processor 1304.Main memory 1308 might also be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 1304. Computing module 1300 might likewise includea read only memory (“ROM”) or other static storage device coupled to bus1302 for storing static information and instructions for processor 1304.

The computing module 1300 might also include one or more various formsof information storage mechanism 1310, which might include, for example,a media drive 1312 and a storage unit interface 1320. The media drive1312 might include a drive or other mechanism to support fixed orremovable storage media 1314. For example, a hard disk drive, a solidstate drive, a magnetic tape drive, an optical disk drive, a CD or DVDdrive (R or RW), or other removable or fixed media drive might beprovided. Accordingly, storage media 1314 might include, for example, ahard disk, a solid state drive, magnetic tape, cartridge, optical disk,a CD or DVD, or other fixed or removable medium that is read by, writtento or accessed by media drive 1312. As these examples illustrate, thestorage media 1314 can include a computer usable storage medium havingstored therein computer software or data.

In alternative embodiments, information storage mechanism 1310 mightinclude other similar instrumentalities for allowing computer programsor other instructions or data to be loaded into computing module 1300.Such instrumentalities might include, for example, a fixed or removablestorage unit 1322 and a storage interface 1320. Examples of such storageunits 1322 and storage interfaces 1320 can include a program cartridgeand cartridge interface, a removable memory (for example, a flash memoryor other removable memory module) and memory slot, a PCMCIA slot andcard, and other fixed or removable storage units 1322 and storageinterfaces 1320 that allow software and data to be transferred from thestorage unit 1322 to computing module 1300.

Computing module 1300 might also include a communications interface1324. Communications interface 1324 might be used to allow software anddata to be transferred between computing module 1300 and externaldevices. Examples of communications interface 1324 might include a modemor softmodem, a network interface (such as an Ethernet, networkinterface card, WiMedia, IEEE 802.XX or other interface), acommunications port (such as for example, a USB port, IR port, RS232port Bluetooth® interface, or other port), or other communicationsinterface. Software and data transferred via communications interface1324 might typically be carried on signals, which can be electronic,electromagnetic (which includes optical) or other signals capable ofbeing exchanged by a given communications interface 1324. These signalsmight be provided to communications interface 1324 via a channel 1328.This channel 1328 might carry signals and might be implemented using awired or wireless communication medium. Some examples of a channel mightinclude a phone line, a cellular link, an RF link, an optical link, anetwork interface, a local or wide area network, and other wired orwireless communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to transitory ornon-transitory media such as, for example, memory 1308, storage unit1320, media 1314, and channel 1328. These and other various forms ofcomputer program media or computer usable media may be involved incarrying one or more sequences of one or more instructions to aprocessing device for execution. Such instructions embodied on themedium are generally referred to as “computer program code” or a“computer program product” (which may be grouped in the form of computerprograms or other groupings). When executed, such instructions mightenable the computing module 1300 to perform features or functions of thepresent application as discussed herein.

The presence of broadening words and phrases such as “one or more,” “atleast,” “but not limited to” or other like phrases in some instancesshall not be read to mean that the narrower case is intended or requiredin instances where such broadening phrases may be absent. The use of theterm “module” does not imply that the components or functionalitydescribed or claimed as part of the module are all configured in acommon package. Indeed, any or all of the various components of amodule, whether control logic or other components, can be combined in asingle package or separately maintained and can further be distributedin multiple groupings or packages or across multiple locations.

Additionally, the various embodiments set forth herein are described interms of exemplary block diagrams, flow charts and other illustrations.As will become apparent to one of ordinary skill in the art afterreading this document, the illustrated embodiments and their variousalternatives can be implemented without confinement to the illustratedexamples. For example, block diagrams and their accompanying descriptionshould not be construed as mandating a particular architecture orconfiguration.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not of limitation. Likewise, the various diagrams maydepict an example architectural or other configuration for thedisclosure, which is done to aid in understanding the features andfunctionality that can be included in the disclosure. The disclosure isnot restricted to the illustrated example architectures orconfigurations, but the desired features can be implemented using avariety of alternative architectures and configurations. Indeed, it willbe apparent to one of skill in the art how alternative functional,logical or physical partitioning and configurations can be implementedto implement the desired features of the present disclosure. Also, amultitude of different constituent module names other than thosedepicted herein can be applied to the various partitions. Additionally,with regard to flow diagrams, operational descriptions and methodclaims, the order in which the steps are presented herein shall notmandate that various embodiments be implemented to perform the recitedfunctionality in the same order unless the context dictates otherwise.

Although the disclosure is described above in terms of various exemplaryembodiments and implementations, it should be understood that thevarious features, aspects and functionality described in one or more ofthe individual embodiments are not limited in their applicability to theparticular embodiment with which they are described, but instead can beapplied, alone or in various combinations, to one or more of the otherembodiments of the disclosure, whether or not such embodiments aredescribed and whether or not such features are presented as being a partof a described embodiment. Thus, the breadth and scope of the presentdisclosure should not be limited by any of the above-described exemplaryembodiments.

What is claimed is:
 1. A method for providing a training load schedulefor peak performance positioning, comprising: providing an initial loadschedule, which is stored in non-volatile memory, to a user, wherein theprovided initial load schedule includes information that allows the userto obtain peak performance positioning for an event to take place on aspecified date in the future; determining a fatigue level of the userusing one or more signals from one or more sensors worn by the user; andcreating and updating a dynamic load schedule stored in non-volatilememory by modifying the initial load schedule based on the determinedfatigue level, wherein the dynamic load schedule includes informationthat allows the user to obtain the peak performance positioning for theevent to take place on the specified date in the future.
 2. The methodof claim 1, wherein the initial load schedule and the dynamic loadschedule comprise at least one of a recommended daily activity level anda recommended fatigue level.
 3. The method of claim 1, wherein creatingand updating the dynamic load schedule occurs in response to determiningthe fatigue level.
 4. The method of claim 3, wherein updating thedynamic load schedule occurs at least once per day.
 5. The method ofclaim 1, wherein creating and updating the dynamic load schedulecomprises positioning the user in an optimal performance zone on thespecified date of the event.
 6. The method of claim 1, furthercomprising maintaining the initial load schedule and the dynamic loadschedule in a calendar.
 7. The method of claim 6, further comprisingdisplaying the initial load schedule and the dynamic load schedule usingthe calendar and at least one of a color-coding representation and anumerical representation.
 8. The method of claim 1, further comprising:receiving an external dynamic load schedule; and comparing the dynamicload schedule to the external dynamic load schedule.
 9. The method ofclaim 1, wherein providing the initial load schedule comprises using asensor configured to be attached to the body of the user.
 10. The methodof claim 1, wherein determining the fatigue level of the user comprisesdetermining an amount of sleep by the user, an amount of physicalactivity by the user, and a heart rate variability (HRV) of the userover a period of time.
 11. The method of claim 1, wherein the one ormore sensors includes a pulse oximeter.
 12. The method of claim 1,further comprising: determining the user's initial fatigue level usingthe one or more sensors worn by the user and using the initial fatiguelevel to determine the initial load schedule.
 13. The method of claim 1,wherein the initial load schedule is determined based on a fitness levelrequired for the event, an estimate of the user's current fitness level,and an amount of time until the specified date of the event.
 14. Themethod of claim 1, further comprising tracking the user's performanceover time; determining a fatigue level at which the user performs best;and updating the dynamic load schedule to position the user at thefatigue level at which the user performs best.